


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCamelCase					:       str			    =     logging.get_logger(__name__)



@add_end_docstrings(A__  )
class A__     ( A__  ):



  """simple docstring"""



  def __init__( self			:  Optional[Any]				,				*lowerCamelCase__			:  Any				,				**lowerCamelCase__			:  int       ):
        super().__init__(*lowerCamelCase__				,				**lowerCamelCase__       )
        requires_backends(self				,				"vision"       )
        self.check_model_type(lowerCamelCase__       )

  def __call__( self			:  List[Any]				,				lowerCamelCase__			:  Union[str, List[str], "Image.Image", List["Image.Image"]]				,				**lowerCamelCase__			:  Optional[int]       ):
        return super().__call__(lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]				,				**lowerCamelCase__			:  int       ):
        return {}, {}, {}

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  Any       ):
        a__	:  str     =   load_image(lowerCamelCase__       )
        a__	:  int     =   image.size
        a__	:  Optional[Any]     =   self.image_processor(images=lowerCamelCase__				,				return_tensors=self.framework       )
        return model_inputs

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  int       ):
        a__	:  List[Any]     =   self.model(**lowerCamelCase__       )
        return model_outputs

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  int       ):
        a__	:  Tuple     =   model_outputs.predicted_depth
        a__	:  Tuple     =   torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1       )				,				size=self.image_size[::-1]				,				mode="bicubic"				,				align_corners=lowerCamelCase__       )
        a__	:  Optional[Any]     =   prediction.squeeze().cpu().numpy()
        a__	:  Tuple     =   (output * 255 / np.max(lowerCamelCase__       )).astype("uint8"       )
        a__	:  List[Any]     =   Image.fromarray(lowerCamelCase__       )
        a__	:  Optional[Any]     =   {}
        a__	:  List[str]     =   predicted_depth
        a__	:  Tuple     =   depth
        return output_dict



"""simple docstring"""


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Any				=      VQModel
       UpperCamelCase_		:       Dict				=      '''sample'''






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				List[str]=(32, 32)      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		4
              UpperCamelCase       			=		3

              UpperCamelCase       			=		floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCAmelCase_      )

              return {"sample": image}






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              return (3, 32, 32)






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              return (3, 32, 32)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		{
                  "block_out_channels": [32, 64],
                  "in_channels": 3,
                  "out_channels": 3,
                  "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                  "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "latent_channels": 3,
              }
              UpperCamelCase       			=		self.dummy_input
              return init_dict, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"							,   output_loading_info=UpperCAmelCase_      )
              self.assertIsNotNone(UpperCAmelCase_      )
              self.assertEqual(len(loading_info["missing_keys"]      )							,   0      )

              model.to(UpperCAmelCase_      )
              UpperCamelCase       			=		model(**self.dummy_input      )

              assert image is not None, "Make sure output is not None"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"      )
              model.to(UpperCAmelCase_      ).eval()

              torch.manual_seed(0      )
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0      )

              UpperCamelCase       			=		torch.randn(1							,   model.config.in_channels							,   model.config.sample_size							,   model.config.sample_size      )
              UpperCamelCase       			=		image.to(UpperCAmelCase_      )
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_      ).sample

              UpperCamelCase       			=		output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              UpperCamelCase       			=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
              # fmt: on
              self.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )





